Integrating ClaimBuster with Cloud Computing for Automated Fake News Detection

Abstract

The rapid spread of fake news across digital platforms poses a serious threat to information integrity and public trust. Traditional fact-checking approaches are often limited by scalability and timeliness. This paper presents the integration of ClaimBuster, an automated fact-checking system, with cloud computing infrastructure to provide a scalable, accurate, and efficient solution for real-time misinformation detection. The proposed system integrates machine learning algorithms and Natural Language Processing (NLP) techniques to classify claims as true or false, supported by cloud-enabled elastic computing resources for handling high-volume, high-velocity data streams. Experimental evaluation demonstrates strong performance in terms of accuracy, precision, recall, and F1-score, with low false positive rates, confirming its robustness in real-world applications. Furthermore, advanced visualization tools, gamification features, and partnerships with media, academic, and governmental institutions enhance user engagement and the overall impact of the system. By prioritizing scalability, adaptability, privacy, and ethics, ClaimBuster within cloud computing offers a comprehensive framework for combating the global challenge of disinformation.